Rigor and reproducibility in human brain organoid research: Where we are and where we need to go.

Human brain organoid models have emerged as a promising tool for studying human brain development and function. These models preserve human genetics and recapitulate some aspects of human brain development, while facilitating manipulation in an in vitro setting. Despite their potential to transform biology and medicine, concerns persist about their fidelity. To fully harness their potential, it is imperative to establish reliable analytic methods, ensuring rigor and reproducibility. Here, we review current analytical platforms used to characterize human forebrain cortical organoids, highlight challenges, and propose recommendations for future studies to achieve greater precision and uniformity across laboratories.

MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology.

Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.

Transcription factor (TF) coordination plays a key role in gene regulation such as protein-protein interactions (PPIs) and DNA co-bindings. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and gene regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embedding (NetREm), to reveal cell-type TF-TF coordination activities for gene regulation. NetREm leverages network-constrained regularization using prior interaction knowledge (e.g., protein, chromatin, TF binding) to analyze single-cell gene expression data. We test NetREm by simulation data and apply it to analyze various cell types in both central and peripheral nerve systems (PNS) such as neuronal, glial and Schwann cells as well as in Alzheimer's disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-target gene candidate links. We also validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat and mouse models and compare our results with additional functional genomic data including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants to TF coordination. NetREm is open-source available at https://github.com/SaniyaKhullar/NetREm .

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. METHOD: To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. RESULTS: We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). CONCLUSION: We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Species-specific FMRP regulation of RACK1 is critical for prenatal cortical development.

Fragile X messenger ribonucleoprotein 1 protein (FMRP) deficiency leads to fragile X syndrome (FXS), an autism spectrum disorder. The role of FMRP in prenatal human brain development remains unclear. Here, we show that FMRP is important for human and macaque prenatal brain development. Both FMRP-deficient neurons in human fetal cortical slices and FXS patient stem cell-derived neurons exhibit mitochondrial dysfunctions and hyperexcitability. Using multiomics analyses, we have identified both FMRP-bound mRNAs and FMRP-interacting proteins in human neurons and unveiled a previously unknown role of FMRP in regulating essential genes during human prenatal development. We demonstrate that FMRP interaction with CNOT1 maintains the levels of receptor for activated C kinase 1 (RACK1), a species-specific FMRP target. Genetic reduction of RACK1 leads to both mitochondrial dysfunctions and hyperexcitability, resembling FXS neurons. Finally, enhancing mitochondrial functions rescues deficits of FMRP-deficient cortical neurons during prenatal development, demonstrating targeting mitochondrial dysfunction as a potential treatment.

Multimodal analysis reveals genes driving neuronal maturation in the primate prefrontal cortex.

The dorsolateral prefrontal cortex (dlPFC) is an evolutionarily derived cortical region in primates critical for high-level cognitive functions and implicated in various neuropsychiatric disorders. The cells that compose the dlPFC, especially excitatory and inhibitory neurons, undergo extensive maturation throughout midfetal and late-fetal development, during which critical neurodevelopmental events, such as circuit assembly and electrophysiological maturation of neurons, occur. Despite the relevance of neuronal maturation in several neurodevelopmental and psychiatric disorders, the molecular mechanisms underlying this process remain largely unknown. Here, we performed an integrated Patch-seq and single-nucleus multiomic analysis of the rhesus macaque dlPFC to identify genes governing neuronal maturation from midfetal to late-fetal development. Our multimodal analysis identified gene pathways and regulatory networks important for the maturation of distinct neuronal populations, including upper-layer intratelencephalicprojecting neurons. We identified genes underlying the maturation of specific electrophysiological properties of these neurons. Furthermore, gene knockdown in organotypic slices revealed that RAPGEF4 regulates the maturation of resting membrane potential and inward sodium current. Using this strategy, we also found that the knockdown of CHD8, a high-confidence autism spectrum disorder risk gene, in human slices led to deficits in neuronal maturation, via the downstream downregulation of several key genes, including RAPGEF4. Our study revealed novel regulators of neuronal maturation during a critical period of prefrontal development in primates and implicated such regulators in molecular processes underlying autism.

CMOT: Cross-Modality Optimal Transport for multimodal inference.

Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell-cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications.

Age-maintained human neurons demonstrate a developmental loss of intrinsic neurite growth ability.

Injury to adult mammalian central nervous system (CNS) axons results in limited regeneration. Rodent studies have revealed a developmental switch in CNS axon regenerative ability, yet whether this is conserved in humans is unknown. Using human fibroblasts from 8 gestational-weeks to 72 years-old, we performed direct reprogramming to transdifferentiate fibroblasts into induced neurons (Fib-iNs), avoiding pluripotency which restores cells to an embryonic state. We found that early gestational Fib-iNs grew longer neurites than all other ages, mirroring the developmental switch in regenerative ability in rodents. RNA-sequencing and screening revealed ARID1A as a developmentally-regulated modifier of neurite growth in human neurons. These data suggest that age-specific epigenetic changes may drive the intrinsic loss of neurite growth ability in human CNS neurons during development. One-Sentence Summary: Directly-reprogrammed human neurons demonstrate a developmental decrease in neurite growth ability.

Elevated levels of FMRP-target MAP1B impair human and mouse neuronal development and mouse social behaviors via autophagy pathway.

Fragile X messenger ribonucleoprotein 1 protein (FMRP) binds many mRNA targets in the brain. The contribution of these targets to fragile X syndrome (FXS) and related autism spectrum disorder (ASD) remains unclear. Here, we show that FMRP deficiency leads to elevated microtubule-associated protein 1B (MAP1B) in developing human and non-human primate cortical neurons. Targeted MAP1B gene activation in healthy human neurons or MAP1B gene triplication in ASD patient-derived neurons inhibit morphological and physiological maturation. Activation of Map1b in adult male mouse prefrontal cortex excitatory neurons impairs social behaviors. We show that elevated MAP1B sequesters components of autophagy and reduces autophagosome formation. Both MAP1B knockdown and autophagy activation rescue deficits of both ASD and FXS patients' neurons and FMRP-deficient neurons in ex vivo human brain tissue. Our study demonstrates conserved FMRP regulation of MAP1B in primate neurons and establishes a causal link between MAP1B elevation and deficits of FXS and ASD.

MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology.

Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.

BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids.

Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.

Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer's disease phenotypes and Covid-19 severity.

Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) for AD and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to AD, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in AD. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of AD-Covid genes using our networks involving known and Covid-19 genes. Our machine learning analysis prioritized 36 AD-Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and AD. Finally, we mapped genome-wide association study SNPs of AD and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an AD-Covid functional genomic resource at the brain region level.

Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex.

BACKGROUND: Neuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the underlying regulatory mechanisms linking them to disorders remain elusive, especially those involving distant genomic elements. Expression quantitative trait loci (eQTLs) constitute a powerful means of providing this missing link. However, most eQTL studies in human brains have focused exclusively on cis-eQTLs, which link variants to nearby genes (i.e., those within 1 Mb of a variant). A complete understanding of disease etiology requires a clearer understanding of trans-regulatory mechanisms, which, in turn, entails a detailed analysis of the relationships between variants and expression changes in distant genes. METHODS: By leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes. RESULTS: We identified ~80,000 candidate trans-eQTLs (at FDR<0.25) that influence the expression of ~10K target genes (i.e., "trans-eGenes"). We found that many variants associated with these candidate trans-eQTLs overlap with known cis-eQTLs. Moreover, for >60% of these variants (by colocalization), the cis-eQTL's target gene acts as a mediator for the trans-eQTL SNP's effect on the trans-eGene, highlighting examples of cis-mediation as essential for trans-regulation. Furthermore, many of these colocalized variants fall into a discernable pattern wherein cis-eQTL's target is a transcription factor or RNA-binding protein, which, in turn, targets the gene associated with the candidate trans-eQTL. Finally, we show that trans-regulatory mechanisms provide valuable insights into psychiatric disorders: beyond what had been possible using only cis-eQTLs, we link an additional 23 GWAS loci and 90 risk genes (using colocalization between candidate trans-eQTLs and schizophrenia GWAS loci). CONCLUSIONS: We demonstrate that the transcriptional architecture of the human brain is orchestrated by both cis- and trans-regulatory variants and found that trans-eQTLs provide insights into brain-disease biology.

Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD.

Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Sustained correction of hippocampal neurogenic and cognitive deficits after a brief treatment by Nutlin-3 in a mouse model of fragile X syndrome.

BACKGROUND: Fragile X syndrome (FXS), the most prevalent inherited intellectual disability and one of the most common monogenic forms of autism, is caused by a loss of fragile X messenger ribonucleoprotein 1 (FMR1). We have previously shown that FMR1 represses the levels and activities of ubiquitin ligase MDM2 in young adult FMR1-deficient mice, and treatment by a MDM2 inhibitor Nutlin-3 rescues both hippocampal neurogenic and cognitive deficits in FMR1-deficient mice when analyzed shortly after the administration. However, it is unknown whether Nutlin-3 treatment can have long-lasting therapeutic effects. METHODS: We treated 2-month-old young adult FMR1-deficient mice with Nutlin-3 for 10 days and then assessed the persistent effect of Nutlin-3 on both cognitive functions and adult neurogenesis when mice were 6-month-old mature adults. To investigate the mechanisms underlying the persistent effects of Nutlin-3, we analyzed the proliferation and differentiation of neural stem/progenitor cells isolated from these mice and assessed the transcriptome of the hippocampal tissues of treated mice. RESULTS: We found that transient treatment with Nutlin-3 of 2-month-old young adult FMR1-deficient mice prevents the emergence of neurogenic and cognitive deficits in mature adult FXS mice at 6 months of age. We further found that the long-lasting restoration of neurogenesis and cognitive function might not be mediated by changing intrinsic properties of adult neural stem cells. Transcriptomic analysis of the hippocampal tissue demonstrated that transient Nultin-3 treatment leads to significant expression changes in genes related to the extracellular matrix, secreted factors, and cell membrane proteins in the FMR1-deficient hippocampus. CONCLUSIONS: Our data indicates that transient Nutlin-3 treatment in young adults leads to long-lasting neurogenic and behavioral changes likely through modulating adult neurogenic niche that impact adult neural stem cells. Our results demonstrate that cognitive impairments in FXS may be prevented by an early intervention through Nutlin-3 treatment.

Altered patterning of trisomy 21 interneuron progenitors.

Individuals with Down syndrome (DS; Ts21), the most common genetic cause of intellectual disability, have smaller brains that reflect fewer neurons at pre- and post-natal stages, implicating impaired neurogenesis during development. Our stereological analysis of adult DS cortex indicates a reduction of calretinin-expressing interneurons. Using Ts21 human induced pluripotent stem cells (iPSCs) and isogenic controls, we find that Ts21 progenitors generate fewer COUP-TFII+ progenitors with reduced proliferation. Single-cell RNA sequencing of Ts21 progenitors confirms the altered specification of progenitor subpopulations and identifies reduced WNT signaling. Activation of WNT signaling partially restores the COUP-TFII+ progenitor population in Ts21, suggesting that altered WNT signaling contributes to the defective development of cortical interneurons in DS.

Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data.

The phenotypes of complex biological systems are fundamentally driven by various multi-scale mechanisms. Multi-modal data, such as single cell multi-omics data, enables a deeper understanding of underlying complex mechanisms across scales for phenotypes. We developed an interpretable regularized learning model, deepManReg, to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then to align multi-modal features onto a common latent space. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also for prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to (1) an image dataset of handwritten digits with multi-features and (2) single cell multi-modal data (Patch-seq data) including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg improved phenotype prediction in both datasets, and also prioritized genes and electrophysiological features for the phenotypes of neuronal cells.